We experimented with 3 different sinc filter frequency scales: Mel, inverse-Mel and linear [1]}. We tested two settings in each case, namely fixed and learnable. Fixed scales are set and left unchanged for both architecture search and train from scratch stages. Learnable scales are initialised in the same way, but the configuration is updated during architecture search. They are then fixed and left unchanged during the train from scratch stage. We also tested a randomly initialised, learnable convolution block denoted Conv_0, in place of sinc filters. The kernel size, stride and the number of output channels for the Conv_0 system are set to the same as that of systems that use sinc filters. The maximum number of masked filters is set to \(F=16\) .
